Aggregating Direct and Indirect Neighbors through Graph Linear Transformations
This addresses the limitation of localized message passing in GNNs for researchers and practitioners working with graph-structured data, though it appears incremental as an alternative aggregation method.
The paper tackles the problem of capturing long-range dependencies in graph neural networks by introducing Graph Linear Transformations, a linear operator that aggregates information from both direct and indirect neighbors without explicit multi-hop enumeration, achieving competitive or superior performance on benchmark datasets.
Graph neural networks (GNN) typically rely on localized message passing, requiring increasing depth to capture long range dependencies. In this work, we introduce Graph Linear Transformations, a linear transformation that realizes direct and indirect feature mixing on graphs through a single, well-defined linear operator derived from the graph structure. By interpreting graphs as walk-summable Gaussian graphical models, we compute these transformations via Gaussian Belief Propagation, enabling each node to aggregate information from both direct and indirect neighbors without explicit enumeration of multi-hop paths. We show that different constructions of the underlying precision matrix induce distinct and interpretable propagation biases, ranging from selective edge-level interactions to uniform structural smoothing, and that Graph Linear Transformations can achieve competitive or superior performance compared to both local message-passing GNNs and dynamic neighborhood aggregation models across homophilic and heterophilic benchmark datasets.